Estimating precipitation at the global scale is vital for Earth system science and disaster response, yet observational coverage and measurement precision remain constrained. Existing solutions provide options that trade off desirable traits such as skill, latency, geographical coverage, temporal coverage, and spatial...
Guy Shalev, S. Rasp, B. Babenko et al.· 0 citations
This agent, Google Earth AI's Geospatial Reasoning agent (GRA), a Gemini-powered research system that combines geospatial data sources, foundation models, and tools to answer questions about the authors' planet is presented.
N. Slonim, Aviv Slobodkin, Gil Fidel et al.· 1 citation
Independent global health case studies of Google Earth AI's Population Dynamics Foundation Model (PDFM) address critical surveillance gaps across domains, and showcase capabilities of geospatial foundation models for public health surveillance.
Arbaaz Muslim, Aviv Slobodkin, Katherine Wheeler-Martin et al.· 0 citations
WeatherNext Cyclones (WN-C), an artificial intelligence-based operational weather model that produces state-of-the-art ensemble forecasts of the track, intensity and size of tropical cyclones worldwide, can predict the track, intensity and size of tropical cyclones with an accuracy gain over leading operational models...
Ferran Alet, Tom R. Andersson, Ilan Price et al.· Nature· 8 citations
Together, WeatherNext 3's capabilities move operational AI-based weather forecasting beyond emulating the traditionally distinct stages of data assimilation, forecasting and post-processing, which helps to further push the frontier of performance and granularity for global weather prediction.
S. Rasp, B. Babenko, Dominic Masters et al.· 3 citations
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